{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "classify ants and bees\n",
    "https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quoting these notes,\n",
    "\n",
    "    In practice, very few people train an entire Convolutional Network from scratch (with random initialization), because it is relatively rare to have a dataset of sufficient size. Instead, it is common to pretrain a ConvNet on a very large dataset (e.g. ImageNet, which contains 1.2 million images with 1000 categories), and then use the ConvNet either as an initialization or a fixed feature extractor for the task of interest.\n",
    "\n",
    "These two major transfer learning scenarios look as follows:\n",
    "\n",
    "    Finetuning the convnet: Instead of random initializaion, we initialize the network with a pretrained network, like the one that is trained on imagenet 1000 dataset. Rest of the training looks as usual.\n",
    "    ConvNet as fixed feature extractor: Here, we will freeze the weights for all of the network except that of the final fully connected layer. This last fully connected layer is replaced with a new one with random weights and only this layer is trained.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# License: BSD\n",
    "# Author: Sasank Chilamkurthy\n",
    "\n",
    "from __future__ import print_function, division\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.optim import lr_scheduler\n",
    "import numpy as np\n",
    "import torchvision\n",
    "from torchvision import datasets, models, transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import os\n",
    "import copy\n",
    "\n",
    "plt.ion()   # interactive mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Data\n",
    "\n",
    "We will use torchvision and torch.utils.data packages for loading the data.\n",
    "\n",
    "The problem we’re going to solve today is to train a model to classify ants and bees. We have about 120 training images each for ants and bees. There are 75 validation images for each class. Usually, this is a very small dataset to generalize upon, if trained from scratch. Since we are using transfer learning, we should be able to generalize reasonably well.\n",
    "\n",
    "This dataset is a very small subset of imagenet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data augmentation and normalization for training\n",
    "# Just normalization for validation\n",
    "data_transforms = {\n",
    "    'train': transforms.Compose([\n",
    "        transforms.RandomResizedCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "    'val': transforms.Compose([\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "}\n",
    "\n",
    "data_dir = '/home/jinbo/gitme/dl-algorithm-coding/data/hymenoptera_data'\n",
    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n",
    "                                          data_transforms[x])\n",
    "                  for x in ['train', 'val']}\n",
    "dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,\n",
    "                                             shuffle=True, num_workers=4)\n",
    "              for x in ['train', 'val']}\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\n",
    "class_names = image_datasets['train'].classes\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize a few images\n",
    "\n",
    "Let’s visualize a few training images so as to understand the data augmentations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def imshow(inp, title=None):\n",
    "    \"\"\"Imshow for Tensor.\"\"\"\n",
    "    inp = inp.numpy().transpose((1, 2, 0))\n",
    "    mean = np.array([0.485, 0.456, 0.406])\n",
    "    std = np.array([0.229, 0.224, 0.225])\n",
    "    inp = std * inp + mean\n",
    "    inp = np.clip(inp, 0, 1)\n",
    "    plt.imshow(inp)\n",
    "    if title is not None:\n",
    "        plt.title(title)\n",
    "    plt.pause(0.001)  # pause a bit so that plots are updated\n",
    "\n",
    "\n",
    "# Get a batch of training data\n",
    "inputs, classes = next(iter(dataloaders['train']))\n",
    "\n",
    "# Make a grid from batch\n",
    "out = torchvision.utils.make_grid(inputs)\n",
    "\n",
    "imshow(out, title=[class_names[x] for x in classes])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training the model\n",
    "\n",
    "Now, let’s write a general function to train a model. Here, we will illustrate:\n",
    "\n",
    "    Scheduling the learning rate\n",
    "    Saving the best model\n",
    "\n",
    "In the following, parameter scheduler is an LR scheduler object from torch.optim.lr_scheduler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
    "    since = time.time()\n",
    "\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    best_acc = 0.0\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
    "        print('-' * 10)\n",
    "\n",
    "        # Each epoch has a training and validation phase\n",
    "        for phase in ['train', 'val']:\n",
    "            if phase == 'train':\n",
    "                scheduler.step()\n",
    "                model.train()  # Set model to training mode\n",
    "            else:\n",
    "                model.eval()   # Set model to evaluate mode\n",
    "\n",
    "            running_loss = 0.0\n",
    "            running_corrects = 0\n",
    "\n",
    "            # Iterate over data.\n",
    "            for inputs, labels in dataloaders[phase]:\n",
    "                inputs = inputs.to(device)\n",
    "                labels = labels.to(device)\n",
    "\n",
    "                # zero the parameter gradients\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                # forward\n",
    "                # track history if only in train\n",
    "                with torch.set_grad_enabled(phase == 'train'):\n",
    "                    outputs = model(inputs)\n",
    "                    _, preds = torch.max(outputs, 1)\n",
    "                    loss = criterion(outputs, labels)\n",
    "\n",
    "                    # backward + optimize only if in training phase\n",
    "                    if phase == 'train':\n",
    "                        loss.backward()\n",
    "                        optimizer.step()\n",
    "\n",
    "                # statistics\n",
    "                running_loss += loss.item() * inputs.size(0)\n",
    "                running_corrects += torch.sum(preds == labels.data)\n",
    "\n",
    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
    "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
    "\n",
    "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n",
    "                phase, epoch_loss, epoch_acc))\n",
    "\n",
    "            # deep copy the model\n",
    "            if phase == 'val' and epoch_acc > best_acc:\n",
    "                best_acc = epoch_acc\n",
    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
    "\n",
    "        print()\n",
    "\n",
    "    time_elapsed = time.time() - since\n",
    "    print('Training complete in {:.0f}m {:.0f}s'.format(\n",
    "        time_elapsed // 60, time_elapsed % 60))\n",
    "    print('Best val Acc: {:4f}'.format(best_acc))\n",
    "\n",
    "    # load best model weights\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing the model predictions\n",
    "\n",
    "Generic function to display predictions for a few images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_model(model, num_images=6):\n",
    "    was_training = model.training\n",
    "    model.eval()\n",
    "    images_so_far = 0\n",
    "    fig = plt.figure()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for i, (inputs, labels) in enumerate(dataloaders['val']):\n",
    "            inputs = inputs.to(device)\n",
    "            labels = labels.to(device)\n",
    "\n",
    "            outputs = model(inputs)\n",
    "            _, preds = torch.max(outputs, 1)\n",
    "\n",
    "            for j in range(inputs.size()[0]):\n",
    "                images_so_far += 1\n",
    "                ax = plt.subplot(num_images//2, 2, images_so_far)\n",
    "                ax.axis('off')\n",
    "                ax.set_title('predicted: {}'.format(class_names[preds[j]]))\n",
    "                imshow(inputs.cpu().data[j])\n",
    "\n",
    "                if images_so_far == num_images:\n",
    "                    model.train(mode=was_training)\n",
    "                    return\n",
    "        model.train(mode=was_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finetuning the convnet\n",
    "\n",
    "Load a pretrained model and reset final fully connected layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/resnet18-5c106cde.pth\" to /home/jinbo/.torch/models/resnet18-5c106cde.pth\n",
      "100%|██████████| 46827520/46827520 [00:04<00:00, 9703891.71it/s] \n"
     ]
    }
   ],
   "source": [
    "model_ft = models.resnet18(pretrained=True)\n",
    "num_ftrs = model_ft.fc.in_features\n",
    "model_ft.fc = nn.Linear(num_ftrs, 2)\n",
    "\n",
    "model_ft = model_ft.to(device)\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Observe that all parameters are being optimized\n",
    "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n",
    "\n",
    "# Decay LR by a factor of 0.1 every 7 epochs\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train and evaluate\n",
    "\n",
    "It should take around 15-25 min on CPU. On GPU though, it takes less than a minute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/24\n",
      "----------\n",
      "train Loss: 0.5720 Acc: 0.7213\n",
      "val Loss: 0.2498 Acc: 0.9150\n",
      "\n",
      "Epoch 1/24\n",
      "----------\n",
      "train Loss: 0.5542 Acc: 0.7869\n",
      "val Loss: 0.4926 Acc: 0.8366\n",
      "\n",
      "Epoch 2/24\n",
      "----------\n",
      "train Loss: 0.3681 Acc: 0.8402\n",
      "val Loss: 0.2816 Acc: 0.8889\n",
      "\n",
      "Epoch 3/24\n",
      "----------\n",
      "train Loss: 0.5980 Acc: 0.7828\n",
      "val Loss: 0.2215 Acc: 0.9412\n",
      "\n",
      "Epoch 4/24\n",
      "----------\n",
      "train Loss: 0.4698 Acc: 0.8033\n",
      "val Loss: 0.3308 Acc: 0.8758\n",
      "\n",
      "Epoch 5/24\n",
      "----------\n",
      "train Loss: 0.5733 Acc: 0.7951\n",
      "val Loss: 0.2305 Acc: 0.9020\n",
      "\n",
      "Epoch 6/24\n",
      "----------\n",
      "train Loss: 0.3754 Acc: 0.8648\n",
      "val Loss: 0.2428 Acc: 0.8824\n",
      "\n",
      "Epoch 7/24\n",
      "----------\n",
      "train Loss: 0.3597 Acc: 0.8566\n",
      "val Loss: 0.2472 Acc: 0.8954\n",
      "\n",
      "Epoch 8/24\n",
      "----------\n",
      "train Loss: 0.3205 Acc: 0.8402\n",
      "val Loss: 0.2462 Acc: 0.8954\n",
      "\n",
      "Epoch 9/24\n",
      "----------\n",
      "train Loss: 0.2671 Acc: 0.8852\n",
      "val Loss: 0.2224 Acc: 0.9085\n",
      "\n",
      "Epoch 10/24\n",
      "----------\n",
      "train Loss: 0.3341 Acc: 0.8770\n",
      "val Loss: 0.2324 Acc: 0.9020\n",
      "\n",
      "Epoch 11/24\n",
      "----------\n",
      "train Loss: 0.2486 Acc: 0.9057\n",
      "val Loss: 0.1988 Acc: 0.9216\n",
      "\n",
      "Epoch 12/24\n",
      "----------\n",
      "train Loss: 0.3061 Acc: 0.8770\n",
      "val Loss: 0.1989 Acc: 0.9281\n",
      "\n",
      "Epoch 13/24\n",
      "----------\n",
      "train Loss: 0.3012 Acc: 0.8566\n",
      "val Loss: 0.2052 Acc: 0.9216\n",
      "\n",
      "Epoch 14/24\n",
      "----------\n",
      "train Loss: 0.3138 Acc: 0.8402\n",
      "val Loss: 0.2069 Acc: 0.9085\n",
      "\n",
      "Epoch 15/24\n",
      "----------\n",
      "train Loss: 0.3545 Acc: 0.8443\n",
      "val Loss: 0.2046 Acc: 0.9020\n",
      "\n",
      "Epoch 16/24\n",
      "----------\n",
      "train Loss: 0.2889 Acc: 0.8893\n",
      "val Loss: 0.1968 Acc: 0.9150\n",
      "\n",
      "Epoch 17/24\n",
      "----------\n",
      "train Loss: 0.3039 Acc: 0.8648\n",
      "val Loss: 0.2144 Acc: 0.9085\n",
      "\n",
      "Epoch 18/24\n",
      "----------\n",
      "train Loss: 0.3199 Acc: 0.8648\n",
      "val Loss: 0.1975 Acc: 0.9216\n",
      "\n",
      "Epoch 19/24\n",
      "----------\n",
      "train Loss: 0.2738 Acc: 0.8934\n",
      "val Loss: 0.2234 Acc: 0.9150\n",
      "\n",
      "Epoch 20/24\n",
      "----------\n",
      "train Loss: 0.2538 Acc: 0.8893\n",
      "val Loss: 0.2354 Acc: 0.9085\n",
      "\n",
      "Epoch 21/24\n",
      "----------\n",
      "train Loss: 0.2574 Acc: 0.8975\n",
      "val Loss: 0.2156 Acc: 0.9085\n",
      "\n",
      "Epoch 22/24\n",
      "----------\n",
      "train Loss: 0.2444 Acc: 0.9057\n",
      "val Loss: 0.2187 Acc: 0.9216\n",
      "\n",
      "Epoch 23/24\n",
      "----------\n",
      "train Loss: 0.2998 Acc: 0.8730\n",
      "val Loss: 0.1907 Acc: 0.9150\n",
      "\n",
      "Epoch 24/24\n",
      "----------\n",
      "train Loss: 0.2956 Acc: 0.8648\n",
      "val Loss: 0.2065 Acc: 0.9085\n",
      "\n",
      "Training complete in 1m 14s\n",
      "Best val Acc: 0.941176\n"
     ]
    }
   ],
   "source": [
    "model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n",
    "                       num_epochs=25)"
   ]
  }
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